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Agent Orchestration Architecture

Community
TheGoat395
agent-orchestration-architecture

Resolve agent ownership and handoff design.

Overview

PublisherTheGoat395
RepositoryCodex-Skills
Skill nameagent-orchestration-architecture
Stars
136
Forks
48
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by TheGoat395 on GitHub. Read the source before you install it.

Installation

Install the Agent Orchestration Architecture AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/TheGoat395/Codex-Skills/tree/main/skills/agent-orchestration-architecture
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/TheGoat395/Codex-Skills.git /tmp/Codex-Skills
mkdir -p .claude/skills
cp -r /tmp/Codex-Skills/skills/agent-orchestration-architecture .claude/skills/agent-orchestration-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Orchestration Architecture in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Orchestration Architecture on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Orchestration Architecture is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Agent Orchestration Architecture

Design in escalating complexity

Choose the lightest design that satisfies the outcome:

  1. Deterministic program or promptless automation.
  2. One agent with bounded tools and structured output.
  3. One manager agent that calls specialists as bounded tools.
  4. Handoffs only when a specialist should own the rest of the interaction.
  5. Durable workflow runtime only when work survives interruptions, waits, or external callbacks.

Do not use an agent swarm as a substitute for an owned workflow.

Define the operating contract

Before implementation, record:

  • outcome, success evidence, and named final-output owner;
  • inputs, output schema, permitted tools, and forbidden actions;
  • model and reasoning choice by decision difficulty, not task size;
  • state owner, memory lifetime, tenant boundary, and source of truth;
  • approval checkpoints, budgets, timeouts, stop conditions, and escalation;
  • retry, idempotency, compensation, and human-handoff behavior.

Parallelize only independent work whose outputs can be reconciled without conflicting writes.

Make control explicit

  • Use a manager when one agent must enforce shared policy, combine specialist work, or own the user-facing answer.
  • Use a handoff when the specialist needs a focused interaction and clear transfer of responsibility.
  • Pass structured task packets, not vague conversation history. Minimize context to the specialist's need.
  • Delegate only a concrete, independent workstream with a defined output. Parallelism can reduce latency, but every subagent performs separate model and tool work and therefore increases usage.
  • Designing a delegation architecture does not authorize spawning agents. Follow current global and native tool constraints: preserve the selected parent model/effort and apply the current global child-model and reasoning policy. Do not freeze a session-specific model or effort into this specialist. Use only supported context controls and sufficient task context; do not override native inheritance constraints for a preferred packet format.
  • Give each specialist the exact sources, constraints, output schema, stopping condition, and useful result-size limit. Ask for distilled findings instead of raw logs or copied source material.
  • Keep the parent thread as the canonical decision owner. Do not spawn several agents to reread the same corpus, and do not use subagents merely to avoid doing a bounded task locally.
  • Use inherited authorization for routine reversible in-scope writes, drafts and configuration. Require explicit authorization for actual sends, spending, binding commitments, publication and materially irreversible actions at their real boundaries; do not reopen an already clear authorization.
  • Design every loop with a maximum attempt count and a useful terminal state.

Required outputs

For a material architecture request, produce the smallest useful combination of an execution diagram, role/tool matrix, state/approval map, operating limits, and evaluation plan. Do not create every artifact for a simple routing decision. Route persistent-context design to $agent-memory-provenance; route quality and release testing to $agent-evaluation-operations; route external events to $integration-contract-reliability.

Optional specialists

For external events, integration-contract-reliability is optional. Without it, define typed inputs/outputs, authentication, idempotency, retries, duplicate handling, uncertain-effect reconciliation and the actual success boundary in the project contract.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agent Orchestration Architecture AI skill do?

Resolve agent ownership and handoff design.

Why use Agent Orchestration Architecture on TypingMind?

Because you install it once and use it with any model. Agent Orchestration Architecture is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Orchestration Architecture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TheGoat395/Codex-Skills/tree/main/skills/agent-orchestration-architecture. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Orchestration Architecture?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Orchestration Architecture?

As many as you like. As long as a model supports skills, you can use Agent Orchestration Architecture with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Orchestration Architecture AI skill free?

Yes. It is published on GitHub by TheGoat395 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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